Assessment of precipitation and near-surface temperature simulation by CMIP6 models in South America

被引:0
|
作者
Reboita, Michelle Simoes [1 ]
Ferreira, Glauber Willian de Souza [1 ]
Ribeiro, Joao Gabriel Martins [1 ]
Ali, Shaukat [2 ]
机构
[1] Univ Fed Itajuba, Inst Nat Resources, Itajuba, Brazil
[2] Hosp Sick Children, Ctr Global Child Hlth, Toronto, ON, Canada
来源
ENVIRONMENTAL RESEARCH-CLIMATE | 2024年 / 3卷 / 02期
关键词
South America; CMIP6; ranking analysis; precipitation; air temperature; REGIONAL CLIMATE; CIRCULATION; SECTION;
D O I
10.1088/2752-5295/ad3fdb
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study evaluated the performance of 50 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the statistical features of precipitation and air temperature in five subdomains of South America during the historical period (1995-2014). Monthly precipitation and temperature simulations were validated with data from the Climate Prediction Center Merged Analysis of Precipitation, the Global Precipitation Climatology Project, and the ERA5 reanalysis. The models' performance was evaluated using a ranking analysis with statistical metrics such as mean, standard deviation, Pearson's spatial correlation, annual cycle amplitude, and linear trend. The analyses considered the representation of precipitation and air temperature separately for each subdomain, the representation for all five regions together, and the joint representation of precipitation and air temperature for all five subdomains. In the Brazilian Amazon, the best-performing models were EC-Earth3-Veg, INM-CM4-8, and INMCM5-0 (precipitation), and IPSL-CM6A-LR, MPI-ESM2-0, and IITM-ESM (temperature). In the La Plata Basin, KACE-1-0-G, ACCESS-CM2, and IPSL-CM6A-LR (precipitation), and GFDL-ESM4, TaiESM1, and EC-Earth3-Veg (temperature) yielded the best simulations. In Northeast Brazil, SAM0-UNICON, CESM2, and MCM-UA-1-0 (precipitation), BCC-CSM2-MR, KACE-1-0-G, and CESM2 (temperature) showed the best results. In Argentine Patagonia, the GCMs ACCESS-CM2, ACCESS-ESM1-5 and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) outperformed. Finally, for Southeast Brazil, the models ACCESS-CM2, ACCESS-ESM1-5, and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) yielded the best simulations. The joint evaluation of the regions and variables indicated that the best models are CESM2, TaiESM1, CMCC-CM2-HR4, FIO-ESM-2-0, and MRI-ESM2-0.
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页数:19
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